基于图像的无脊椎动物自动识别和生物量估计
Automatic image-based identification and biomass estimation of invertebrates
摘要：
了解生物群落如何响应环境变化是生态学和生态系统管理的一个关键挑战。昆虫数量的明显下降需要更多的生物监测，但分类群的耗时分类和鉴定对可以处理多少昆虫样本构成了很大的限制。反过来，这影响了绘制无脊椎动物多样性地图的工作规模。鉴于计算机视觉的最新进展，我们建议用基于图像的自动技术取代基于人类专家的分类和识别的标准手动方法。我们描述了一个机器人使能的基于图像的识别机器，它可以自动化的无脊椎动物识别，生物量估计和样品分选的过程。我们使用的成像设备，以产生一个全面的图像数据库的陆生节肢动物物种。我们使用这个数据库来测试分类的准确性，即如何以及一个标本的物种身份可以从机器拍摄的图像预测。我们还测试了分类精度对相机设置（光圈和曝光时间）的敏感性，以便以最佳图像质量向前迈进。我们使用最先进的Resnet-50和InceptionV 3 CNN进行分类任务。初始数据集的结果非常有希望（$\overline{ACC}=0.980$）。该系统是通用的，可以很容易地用于其他群体的无脊椎动物以及。因此，我们的研究结果为生成更多关于无脊椎动物丰度、多样性和生物量的时空变化数据铺平了道路。
英文摘要：
Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map invertebrate diversity altogether. Given recent advances in computer vision, we propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We describe a robot-enabled image-based identification machine, which can automate the process of invertebrate identification, biomass estimation and sample sorting. We use the imaging device to generate a comprehensive image database of terrestrial arthropod species. We use this database to test the classification accuracy i.e. how well the species identity of a specimen can be predicted from images taken by the machine. We also test sensitivity of the classification accuracy to the camera settings (aperture and exposure time) in order to move forward with the best possible image quality. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task. The results for the initial dataset are very promising ($\overline{ACC}=0.980$). The system is general and can easily be used for other groups of invertebrates as well. As such, our results pave the way for generating more data on spatial and temporal variation in invertebrate abundance, diversity and biomass.
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